There are two lectures each week: Tuesday 9.00-10.45 hrs and Thursday 13.15-15.00 hrs.
In the planning below, lecture 37A is the Tuesday lecture in week 37 etc.
There are Computer Lab sessions (P) in weeks 39-43.
|37A||Introduction (Slides)||A. Feelders, H. Daniels, M. Holsheimer Methodological and Practical Aspects of Data Mining|
|37B||Classification Trees (1) (Slides)||Lecture notes Classification Trees: section 1-3.3|
|38A||Classification Trees (2) (Slides)||Lecture notes Classification Trees: section 3.4-3.5|
|38B||Clustering (Slides)||The Slides|
|39A||Self-Organizing Maps (1) [Guest Lecture by Dr. Markus Schedl] (Slides)||The Slides|
|39B||Self-Organizing Maps (2) [Guest Lecture by Dr. Markus Schedl]||The Slides|
|39P||Computer Lab||Work on assignment 1|
|40A||Graphical Models (1) (Slides)||Lecture Notes Graphical Models (Part 1)|
|40B||Graphical Models (2) (Slides)||Lecture Notes Graphical Models (Part 1)|
|41A||Graphical Models (3)/Exercise Class (Exercises)|
|41B||Exercise Class (see 41A for exercises) (Solutions)|
|42A||Bayesian Networks (1) (Slides)||Lecture Notes on Graphical Models (Part 2): Sections 1-4|
|42B||Bayesian Networks (2) (Slides)||Lecture Notes on Graphical Models (Part 2): Sections 5-6|
|43A||Bayesian Network Classifiers (Slides)||Article N. Friedman et al. (see Literature)|
|43B||Frequent Itemset Mining (Slides)||Lecture Notes Frequent Item Set Mining (see Literature).|
|44A||Subgroup Discovery (Slides)||Lecture notes Rule induction by bump hunting (see Literature).
Article J.H. Friedman and N.I. Fisher (see Literature)
|44B||Exercise Class: Exercises (Solutions)|